Securing Large Language Models: LLM Application Security

As generative AI becomes central to modern software, securing these systems against unique vulnerabilities is more critical than ever. Traditional security measures are no longer enough to protect applications powered by large language models (LLMs). This text-only course provides a clear foundation in LLM security, guiding you from basic terminology to robust defense strategies. You will understand how these models work, identify their unique attack surfaces, and learn how to implement safeguards to protect your data and users. What you'll learn: Understand foundational LLM concepts and how generative AI models process inputs; Identify critical vulnerabilities like prompt injection, data poisoning, and insecure output handling; Apply the OWASP Top 10 framework specifically tailored for LLM applications; Secure Retrieval-Augmented Generation (RAG) pipelines and vector database integrations; Configure safe system prompts and input/output sanitization mechanisms; Implement governance and data privacy controls to prevent sensitive information leakage. The course begins by exploring the core architecture of large language models and their common integration patterns. From there, you will progress through theoretical threat scenarios, learning how to analyze vulnerabilities and apply defensive engineering techniques through written examples and guided conceptual exercises. Designed for security beginners, developers, and IT professionals looking to understand AI security, this course requires no prior background in machine learning. Start building safer, more resilient AI integrations today.